Exploring Human Brain Metabolism via Genome-Scale Metabolic Modeling with Highlights on Multiple Sclerosis.

Abstract

Cerebral dysfunctions give rise to a wide range of neurological diseases due to the structural and functional complexity of the human brain stemming from the interactive cellular metabolism of its specific cells, including neurons and glial cells. In parallel with advances in isolation and measurement technologies, genome-scale metabolic models (GEMs) have become a powerful tool in the studies of systems biology to provide critical insights into the understanding of sophisticated eukaryotic systems. In this study, brain cell-specific GEMs were reconstructed for neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte precursor cells by integrating single-cell RNA-seq data and global Human1 via a task-driven integrative network inference for tissues (tINIT) algorithm. Then, intercellular reactions among neurons, astrocytes, microglia, and oligodendrocytes were added to generate a combined brain model, iHumanBrain2690. This brain network was used in the prediction of metabolic alterations in glucose, ketone bodies, oxygen change, and reporter metabolites. Glucose supplementation increased the subsystems' activities in glycolysis, and ketone bodies elevated those in the TCA cycle and oxidative phosphorylation. Reporter metabolite analysis identified L-carnitine and arachidonate as the top reporter metabolites in gray and white matter microglia in multiple sclerosis (MS), respectively. Carbamoyl-phosphate was found to be the top reporter metabolite in primary progressive MS. Taken together, single and integrated iHumanBrain2690 metabolic networks help us elucidate complex metabolism in brain physiology and homeostasis in health and disease.